Handling Severity Levels of Multiple Co-Occurring Cotton Plant Diseases Using Improved YOLOX Model

Automatic detection of plant diseases has emerged as a challenging field in the last decade. Computer vision-based advancements have helped in the timely and accurate identification of diseases, making possible an appropriate treatment and hence ensuring an increased yield. Diseases attack in different formations on a plant; the most severe being multiple diseases appearing on a single leaf. Moreover, as various diseases progress, they generate similar-looking symptoms making the task of identification further difficult. This work addresses these two problems with the help of an improved YOLOX model. We propose a modified Spatial Pyramid Pooling (SPP) layer to effectively extract relevant features at various scales from the training data. It is achieved by concatenating multilevel features pooled from smaller to larger scales. To enhance the generalization capability of the design, various skip connections are also introduced. To improve the network convergence and detection accuracy,
$\alpha _{IoU}$
based regression loss function was employed. A dataset composed of 1, 112 cotton plant images with co-occurring diseases along with their progressive severity levels was collected from the Southern Punjab region of Pakistan. Apart from healthy images, the dataset comprises three severity stages of cotton leaf curl with co-occurring cotton sooty mold stress on a single leaf image. Experimental results revealed that our proposed improved SPP-based YOLOX-s model achieved 73.13% mAP on our self-collected dataset and achieved 3.27% better test accuracy than the original YOLOX model.